AIMC Topic: Software

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Geometric deep learning framework for de novo genome assembly.

Genome research
The critical stage of every de novo genome assembler is identifying paths in assembly graphs that correspond to the reconstructed genomic sequences. The existing algorithmic methods struggle with this, primarily due to repetitive regions causing comp...

PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery.

Journal of chemical information and modeling
The advent of powerful machine learning algorithms as well as the availability of high volume of pharmacological data has given new fuel to QSAR, opening new unprecedented options for deriving highly predictive models for assisting the rationale desi...

DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.

BMC bioinformatics
BACKGROUND: A significant challenge in precision medicine is confidently identifying mutations detected in sequencing processes that play roles in disease treatment or diagnosis. Furthermore, the lack of representativeness of single nucleotide varian...

MIDAA: deep archetypal analysis for interpretable multi-omic data integration based on biological principles.

Genome biology
High-throughput multi-omic molecular profiling allows the probing of biological systems at unprecedented resolution. However, integrating and interpreting high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biolo...

DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.

BMC bioinformatics
Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural net...

Data splitting to avoid information leakage with DataSAIL.

Nature communications
Information leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model's training, it risks memorizing the training data instead of learning generalizable prope...

Real-life benefit of artificial intelligence-based fracture detection in a pediatric emergency department.

European radiology
OBJECTIVES: This study aimed to evaluate the performance of an artificial intelligence (AI)-based software for fracture detection in pediatric patients within a real-life clinical setting. Specifically, it sought to assess (1) the stand-alone AI perf...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Journal of microbiological methods
Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential genes in pathogens and non-pathogenic genes. Since, identification of therapeutic targets and understand...

scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

BMC genomics
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First...

CGLoop: a neural network framework for chromatin loop prediction.

BMC genomics
BACKGROUND: Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patter...